A Novel Intraventricular Tumor Removal Device: Development of a Compression-Aided Mechanism Capable of Simultaneously Resecting and Coagulating Tissue
Bibliographic record
Abstract
Abstract Resecting intraventricular brain tumors via a traditional surgical approach is a highly invasive procedure, with reported morbidity rates of up to 70%. As such, powered tissue resection devices have been developed to rapidly fragment and remove these tumors endoscopically. A key shortcoming of these devices is that they typically cannot be used when a tumor is vascularized, because unmanageable levels of bleeding are encountered during tumor fragmentation. The objective of this research was thus to develop a novel resection device that could simultaneously heat and thereby coagulate the tumor as it is fragmented. To accomplish this without reducing the tissue resection rate, the device had to coagulate tissue in less than 50 ms. Finite element modeling (FEM) found that by concurrently compressing tissue and applying a radio frequency (RF) current, tissue coagulation could be achieved in 21.9 ms. Based on these results, we developed a design that removes tissue by cyclically compressing, coagulating, and fragmenting it. A series of prototypes were first used to optimize the design's resection and coagulation capabilities. Finally, a single-cycle version of the device was tested on ex vivo samples. The tool coagulated tissue to a depth consistent with hemostasis while simultaneously removing as much tissue as existing resection devices. At optimal settings, coagulation did not extend deeper than 192±7 μm into the samples, less than the thermal injury depth for neurosurgical coagulation tools. In conclusion, this work represents a strong step toward the creation of an endoscopic tool that can rapidly resect vascularized intraventricular tumors.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".